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Towards Machine Learning Systems Design

Lessons from Computational Biology

Neil D. Lawrence

Mathematical Genomics Away Day

What is Machine Learning?

\[ \text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}\]

  • data : observations, could be actively or passively acquired (meta-data).
  • model : assumptions, based on previous experience (other data! transfer learning etc), or beliefs about the regularities of the universe. Inductive bias.
  • prediction : an action to be taken or a categorization or a quality score.

What is Machine Learning?

\[\text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}\]

  • To combine data with a model need:
  • a prediction function \(\mappingFunction (\cdot)\) includes our beliefs about the regularities of the universe
  • an objective function \(\errorFunction (\cdot)\) defines the cost of misprediction.

Machine Learning

  • Driver of two different domains:
    1. Data Science: arises from the fact that we now capture data by happenstance.
    2. Artificial Intelligence: emulation of human behaviour.
  • Connection: Internet of Things

Machine Learning

  • Driver of two different domains:
    1. Data Science: arises from the fact that we now capture data by happenstance.
    2. Artificial Intelligence: emulation of human behaviour.
  • Connection: Internet of Things

Machine Learning

  • Driver of two different domains:
    1. Data Science: arises from the fact that we now capture data by happenstance.
    2. Artificial Intelligence: emulation of human behaviour.
  • Connection: Internet of People

Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data (1981/1/28)

What does Machine Learning do?

  • ML Automates through Data
    • Strongly related to statistics.
    • Field underpins revolution in data science and AI
  • With AI:
    • logic, robotics, computer vision, speech
  • With Data Science:
    • databases, data mining, statistics, visualization

What does Machine Learning do?

  • Automation scales by codifying processes and automating them.
  • Need:
    • Interconnected components
    • Compatible components
  • Early examples:
    • cf Colt 45, Ford Model T

Codify Through Mathematical Functions

  • How does machine learning work?
  • Jumper (jersey/sweater) purchase with logistic regression

\[ \text{odds} = \frac{p(\text{bought})}{p(\text{not bought})} \]

\[ \log \text{odds} = \beta_0 + \beta_1 \text{age} + \beta_2 \text{latitude}.\]

Codify Through Mathematical Functions

  • How does machine learning work?
  • Jumper (jersey/sweater) purchase with logistic regression

\[ p(\text{bought}) = \sigmoid{\beta_0 + \beta_1 \text{age} + \beta_2 \text{latitude}}.\]

Codify Through Mathematical Functions

  • How does machine learning work?
  • Jumper (jersey/sweater) purchase with logistic regression

\[ p(\text{bought}) = \sigmoid{\boldsymbol{\beta}^\top \inputVector}.\]

Codify Through Mathematical Functions

  • How does machine learning work?
  • Jumper (jersey/sweater) purchase with logistic regression

\[ \dataScalar = \mappingFunction\left(\inputVector, \boldsymbol{\beta}\right).\]

We call \(\mappingFunction(\cdot)\) the prediction function.

Fit to Data

  • Use an objective function

\[\errorFunction(\boldsymbol{\beta}, \dataMatrix, \inputMatrix)\]

  • E.g. least squares \[\errorFunction(\boldsymbol{\beta}, \dataMatrix, \inputMatrix) = \sum_{i=1}^\numData \left(\dataScalar_i - \mappingFunction(\inputVector_i, \boldsymbol{\beta})\right)^2.\]

Two Components

  • Prediction function, \(\mappingFunction(\cdot)\)
  • Objective function, \(\errorFunction(\cdot)\)

Supply Chain

Cromford

Deep Freeze

Deep Freeze

Machine Learning in Supply Chain

  • Supply chain: Large Automated Decision Making Network
  • Major Challenge:
    • We have a mechanistic understanding of supply chain.
    • Machine learning is a data driven technology.

Deploying Artificial Intelligence

  • Challenges in deploying AI.
  • Currently this is in the form of “machine learning systems”

Internet of People

  • Fog computing: barrier between cloud and device blurring.
    • Computing on the Edge
  • Complex feedback between algorithm and implementation

Deploying ML in Real World: Machine Learning Systems Design

  • Major new challenge for systems designers.
  • Internet of Intelligence but currently:
    • AI systems are fragile

Machine Learning Systems Design

Fragility of AI Systems

  • They are componentwise built from ML Capabilities.
  • Each capability is independently constructed and verified.
    • Pedestrian detection
    • Road line detection
  • Important for verification purposes.

Pigeonholing

Robust

  • Need to move beyond pigeonholing tasks.
  • Need new approaches to both the design of the individual components, and the combination of components within our AI systems.

Rapid Reimplementation

  • Whole systems are being deployed.
  • But they change their environment.
  • The experience evolved adversarial behaviour.

Machine Learning Systems Design

Adversaries

  • Stuxnet
  • Mischevious-Adversarial

An Intelligent System

Joint work with M. Milo

An Intelligent System

Joint work with M. Milo

Peppercorns

  • A new name for system failures which aren’t bugs.
  • Difference between finding a fly in your soup vs a peppercorn in your soup.

Peppercorns

Turnaround And Update

  • There is a massive need for turn around and update
  • A redeploy of the entire system.
    • This involves changing the way we design and deploy.
  • Interface between security engineering and machine learning.

Conclusion

  • The Cell is a Micro Supply Chain.
    • Analyzing cell data has a lot in common with analyzing supply chain data.
    • In Biology you are fortunate to have many cells (destructive testing).
  • In Supply Chain we find it easier to deploy modificiations for the system.
    • Downstream effects are complex and need monitoring.
    • Life is really good at dealing with evolving environments … our designs not so much.

Thanks!

References